{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe3541b0588>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = make_classification(n_samples=100, #生成100个样本\n",
    "                          n_features=2, #包含2个特征,即二维数据\n",
    "                          n_redundant=0, #添加冗余特征0个\n",
    "                          n_informative=2,#包含信息的特征是2个\n",
    "                          random_state=1,#随即模式1\n",
    "                          n_clusters_per_class=1#每个簇内包含的标签类别有1个\n",
    "                          )\n",
    "\n",
    "#在这里可以查看一下X和y,其中X是100行带有两个2特征的数据,y是二分类标签\n",
    "#也可以画出散点图来观察一下X中特征的分布\n",
    "plt.scatter(x[:,0], x[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe354125438>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#从图上可以看出,生成的二分型数据的两个簇离彼此很远,这样不利于我们测试分类器的效果,因此我们使用np生成随机数组,通过让已经生成的二分型数据点加减0~1之间的随机数,使数据分布变得更散更稀疏\n",
    "#注意,这个过程只能够运行一次,因为多次运行之后X会变得非常稀疏,两个簇的数据会混合在一起,分类器的效应会继续下降\n",
    "\n",
    "rng = np.random.RandomState(2) #生成一种随机模式\n",
    "x += 2 * rng.uniform(size=x.shape) #加减0~1之间的随机数\n",
    "linearly_separable = (x, y) #生成了新的X,依然可以画散点图来观察一下特征的分布\n",
    "plt.scatter(x[:,0],x[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(array([[ 0.03159499,  0.98698776],\n",
      "       [ 2.11509784, -0.04624397],\n",
      "       [ 0.88248972, -0.07575606],\n",
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      "       [ 0.82954503, -0.53932149],\n",
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      "       [ 2.00344509, -0.3091496 ],\n",
      "       [ 1.50009616, -0.74636376],\n",
      "       [ 1.22540835,  1.19793017],\n",
      "       [ 0.46730819,  0.54703192],\n",
      "       [-0.36653222,  1.11971633],\n",
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      "       [ 0.30430746,  0.82373935],\n",
      "       [-0.23712492,  0.77876034],\n",
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      "       [ 0.95291428, -0.41976564],\n",
      "       [-1.31850034,  0.42311235],\n",
      "       [-1.29681764,  0.18414709]]), array([0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
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      "       1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0])), (array([[-0.38289117, -0.09084004],\n",
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      "       [-0.32179572, -0.80553536],\n",
      "       [-1.07478639, -0.733362  ],\n",
      "       [ 0.76758455, -0.43498783],\n",
      "       [-0.47475234, -0.33813186],\n",
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      "       [ 0.65916696,  0.20773634],\n",
      "       [-0.77795003,  0.1326555 ],\n",
      "       [ 0.47025454, -0.31228748],\n",
      "       [-0.04246799,  0.26555446],\n",
      "       [-0.72405954,  0.48807185],\n",
      "       [-0.36960005, -1.06514028],\n",
      "       [ 0.17833327, -0.49718972],\n",
      "       [-0.93927864, -0.41951638],\n",
      "       [ 0.50914152, -0.70977467],\n",
      "       [-0.05569852, -0.82162607],\n",
      "       [-0.11214579,  0.72197044],\n",
      "       [ 0.80463921, -0.15221296],\n",
      "       [ 0.08261487, -0.11749021],\n",
      "       [ 0.20349541, -0.37396789],\n",
      "       [ 0.13864693, -0.23905642],\n",
      "       [ 0.32785307, -1.00769037],\n",
      "       [ 0.88944061, -0.39117628],\n",
      "       [-0.05837947,  0.28487039],\n",
      "       [-1.0673653 ,  0.2204006 ],\n",
      "       [-0.60071345, -0.69545189],\n",
      "       [-0.03972324, -0.40936056],\n",
      "       [ 0.39742085,  0.20621162],\n",
      "       [-0.36941154,  0.0129811 ],\n",
      "       [ 0.03573703,  0.46666229],\n",
      "       [-0.56814999, -0.41288419],\n",
      "       [ 0.41047299, -0.73640868],\n",
      "       [ 0.88249707, -0.69004404],\n",
      "       [ 0.06579822, -0.50458395],\n",
      "       [-0.75737223, -0.0724028 ],\n",
      "       [ 0.18316966,  0.08722007],\n",
      "       [ 0.67248314, -0.41892665],\n",
      "       [ 0.25898723,  0.39688645],\n",
      "       [-1.1312983 ,  0.4810614 ],\n",
      "       [ 1.0592844 ,  0.64490287],\n",
      "       [ 0.41019663,  0.38790198],\n",
      "       [ 0.95142029, -0.04089983],\n",
      "       [-0.60492988,  0.43950906],\n",
      "       [ 0.23314762, -0.81785711],\n",
      "       [ 0.91067331,  0.30702075],\n",
      "       [-0.45026472, -0.03724104],\n",
      "       [-0.81396121, -0.64733959],\n",
      "       [-0.23191338,  0.50533992],\n",
      "       [-0.59760983,  0.28023168],\n",
      "       [ 0.73960166, -0.84270281],\n",
      "       [ 0.57294659, -0.31198928],\n",
      "       [ 0.24821133, -0.54784509],\n",
      "       [ 0.52127802,  0.94108005],\n",
      "       [ 0.33973198,  0.10609978],\n",
      "       [ 1.05339036, -0.02197593],\n",
      "       [ 0.01327466, -0.63379502],\n",
      "       [ 0.2422589 ,  0.49032064],\n",
      "       [-0.89266612,  0.6345076 ],\n",
      "       [ 0.1672566 ,  0.23548462],\n",
      "       [-0.05611705,  0.38834099],\n",
      "       [ 0.84695486,  0.81435811],\n",
      "       [ 0.29976195, -0.07943031],\n",
      "       [-0.1404762 ,  0.72486032],\n",
      "       [-0.05482024,  0.18417328],\n",
      "       [-0.24643884, -0.43283337],\n",
      "       [-0.23460645,  0.6409442 ],\n",
      "       [-1.13184893, -0.61964942],\n",
      "       [-0.92413821, -0.45302089],\n",
      "       [ 0.2225745 ,  0.77052597],\n",
      "       [-0.69453765,  0.53014147],\n",
      "       [-1.0362509 ,  0.77339965],\n",
      "       [ 0.51880585,  0.30152232],\n",
      "       [-0.77429541,  0.02553767],\n",
      "       [ 0.71468326,  0.56869015],\n",
      "       [-0.33875274,  0.46826063],\n",
      "       [-0.34749244,  0.13441418],\n",
      "       [ 1.12980796,  0.04281936],\n",
      "       [-0.38308979,  0.79116812],\n",
      "       [-0.07425141,  0.2184625 ],\n",
      "       [-0.44945202, -0.05722266],\n",
      "       [ 0.85783288,  0.63778888],\n",
      "       [-0.47486203, -0.22498112],\n",
      "       [ 0.12627243,  0.86978412],\n",
      "       [-0.64736458, -0.36342437],\n",
      "       [ 0.47440459,  1.01101585],\n",
      "       [-0.38565772, -0.81031183]]), array([1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
      "       1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
      "       1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
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      "       1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0])), (array([[-0.17749658,  0.93049627],\n",
      "       [ 1.97742406,  1.76615529],\n",
      "       [ 1.80002379,  1.7003428 ],\n",
      "       [-0.77083686,  2.35916348],\n",
      "       [-0.30800901,  1.59406316],\n",
      "       [ 0.61472141,  2.43489824],\n",
      "       [ 1.0909431 ,  2.21663402],\n",
      "       [-0.83488954,  2.35868875],\n",
      "       [ 2.47946499,  1.33418967],\n",
      "       [ 1.19926597,  1.82681392],\n",
      "       [ 2.18506184,  0.8226875 ],\n",
      "       [ 1.68423203,  1.0411311 ],\n",
      "       [ 1.14621135,  2.07048527],\n",
      "       [ 1.35596945,  0.56802498],\n",
      "       [ 1.38520748,  1.82607238],\n",
      "       [ 1.86729217,  1.18692699],\n",
      "       [ 0.31227376,  2.24605353],\n",
      "       [-0.08183484,  1.70065511],\n",
      "       [ 0.90484472,  2.60683016],\n",
      "       [-0.4358609 ,  2.0151414 ],\n",
      "       [ 0.81441196,  2.13546784],\n",
      "       [ 3.10874312,  2.26786504],\n",
      "       [-0.25574217,  1.71281146],\n",
      "       [ 1.88663027,  2.72089513],\n",
      "       [ 2.29503547,  3.89267759],\n",
      "       [-0.11415626,  0.85593357],\n",
      "       [ 1.69703537,  3.47717799],\n",
      "       [ 1.83855775,  0.92667213],\n",
      "       [ 1.60298807,  1.3018436 ],\n",
      "       [ 1.13803215,  2.82955007],\n",
      "       [ 0.57002738,  1.82202469],\n",
      "       [ 0.25130172,  1.32225048],\n",
      "       [-0.39833078,  1.984345  ],\n",
      "       [ 1.6291326 ,  0.88168182],\n",
      "       [ 3.10503585,  1.74000633],\n",
      "       [-0.00566874,  1.12039041],\n",
      "       [ 1.36866557,  2.13964438],\n",
      "       [ 2.69839961,  3.54932382],\n",
      "       [ 2.00170838,  1.09862314],\n",
      "       [ 2.43627708,  1.43769476],\n",
      "       [-0.30996938,  0.91256444],\n",
      "       [ 0.08528148,  1.72286096],\n",
      "       [ 2.06235803,  1.60351529],\n",
      "       [ 1.61026984,  2.06899308],\n",
      "       [ 1.39875914,  0.69295034],\n",
      "       [ 1.68417195,  1.05499076],\n",
      "       [ 0.6032637 ,  2.18625056],\n",
      "       [-0.80090405,  1.89873999],\n",
      "       [ 2.28622906,  2.00485199],\n",
      "       [-1.08107738,  2.77507784],\n",
      "       [ 0.6731465 ,  1.08374453],\n",
      "       [ 2.96970576,  2.12322913],\n",
      "       [ 0.67950668,  2.09864274],\n",
      "       [-0.44781177,  3.18420372],\n",
      "       [-0.50033854,  2.02105531],\n",
      "       [ 1.85307118,  3.66612496],\n",
      "       [ 1.72186424,  1.78055874],\n",
      "       [ 2.25033483,  3.12143112],\n",
      "       [ 0.31356908,  1.35127949],\n",
      "       [-0.28410691,  1.85727304],\n",
      "       [ 2.12915656,  2.58463127],\n",
      "       [ 1.5764227 ,  2.01987282],\n",
      "       [-1.07819549,  1.40147405],\n",
      "       [ 1.19768119,  2.40249075],\n",
      "       [ 1.37690112,  1.14985842],\n",
      "       [-1.66006488,  1.46313474],\n",
      "       [ 1.24390011,  1.46004628],\n",
      "       [ 1.87922305,  1.72262314],\n",
      "       [ 2.90850791,  2.94159145],\n",
      "       [-0.48122613,  1.79143762],\n",
      "       [ 2.4374304 ,  1.34535331],\n",
      "       [ 2.56579874,  0.99334358],\n",
      "       [ 2.44899241,  2.49072727],\n",
      "       [ 0.46034247,  0.68402612],\n",
      "       [-0.39068268,  2.13223301],\n",
      "       [-1.26676594,  1.15606381],\n",
      "       [ 1.41882694,  2.57154527],\n",
      "       [-0.56760436,  0.99010229],\n",
      "       [-1.80173745,  1.23149132],\n",
      "       [ 0.18035906,  1.25587797],\n",
      "       [ 2.85271766,  1.84520561],\n",
      "       [ 1.84588723,  1.15083733],\n",
      "       [ 2.2332857 ,  3.1528139 ],\n",
      "       [-0.55018044,  1.08169147],\n",
      "       [-0.79089021,  1.65085521],\n",
      "       [-0.13199329,  1.94007796],\n",
      "       [ 0.70257257,  1.35701296],\n",
      "       [ 0.27672155,  2.67298668],\n",
      "       [ 0.61021587,  2.1651486 ],\n",
      "       [-0.53000368,  2.01943164],\n",
      "       [ 1.83727888,  2.1110129 ],\n",
      "       [ 0.28416019,  2.64492964],\n",
      "       [ 1.89688379,  2.26044693],\n",
      "       [ 2.06813613,  2.6101669 ],\n",
      "       [-1.72274127,  1.27907652],\n",
      "       [ 2.63238161,  1.27130508],\n",
      "       [-0.04025574,  1.7827081 ],\n",
      "       [-0.78745318,  1.40035688],\n",
      "       [ 2.70244116,  1.58744358],\n",
      "       [ 1.2909693 ,  2.75193673]]), array([1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n",
      "       1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
      "       1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
      "       0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1,\n",
      "       1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1]))]\n"
     ]
    }
   ],
   "source": [
    "datasets = [make_moons(noise=0.3, random_state=0),\n",
    "           make_circles(noise=0.2, factor=0.5, random_state=1),\n",
    "           linearly_separable]\n",
    "print(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(len(datasets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "print(len(datasets[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "print(len(datasets[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "print(len(datasets[0][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03159499,  0.98698776],\n",
       "       [ 2.11509784, -0.04624397],\n",
       "       [ 0.88248972, -0.07575606],\n",
       "       [-0.0551441 , -0.03733246],\n",
       "       [ 0.82954503, -0.53932149],\n",
       "       [ 2.11285708,  0.66208353],\n",
       "       [ 0.5696927 ,  0.33744136],\n",
       "       [ 0.95217454, -0.75307471],\n",
       "       [-0.02922115,  0.39287336],\n",
       "       [ 1.71579557,  0.36069454],\n",
       "       [-0.27883014,  0.61420539],\n",
       "       [ 2.00344509, -0.3091496 ],\n",
       "       [ 1.50009616, -0.74636376],\n",
       "       [ 1.22540835,  1.19793017],\n",
       "       [ 0.46730819,  0.54703192],\n",
       "       [-0.36653222,  1.11971633],\n",
       "       [ 0.30124459,  1.23315697],\n",
       "       [ 0.30430746,  0.82373935],\n",
       "       [-0.23712492,  0.77876034],\n",
       "       [ 1.16339832,  0.55290238],\n",
       "       [-0.59702417,  0.6572361 ],\n",
       "       [ 0.46356735, -0.1951543 ],\n",
       "       [ 0.68451111,  0.75287685],\n",
       "       [ 0.70515699, -0.45892444],\n",
       "       [-0.65805008, -0.12944211],\n",
       "       [-0.74662946, -0.3829632 ],\n",
       "       [ 0.60585226,  0.31252842],\n",
       "       [ 2.18137168, -0.02291747],\n",
       "       [ 1.91980633,  0.17247329],\n",
       "       [ 1.4834364 , -0.10517023],\n",
       "       [ 0.47404723, -0.14575067],\n",
       "       [ 1.05614605, -1.03570207],\n",
       "       [ 1.86500732, -0.01107874],\n",
       "       [ 0.4194721 , -0.32478101],\n",
       "       [ 0.06873258,  0.56648467],\n",
       "       [-0.17332432,  1.00215131],\n",
       "       [ 0.12412421,  1.00062892],\n",
       "       [ 0.18121142,  0.24717743],\n",
       "       [-0.25451559,  0.19317272],\n",
       "       [ 1.02580668, -0.62574566],\n",
       "       [ 1.52002143, -0.04515581],\n",
       "       [ 0.64174037, -0.39369468],\n",
       "       [-0.87615589,  0.0465662 ],\n",
       "       [-1.06964997,  0.13666025],\n",
       "       [ 1.02658765,  0.37782458],\n",
       "       [ 0.93131325,  1.38517841],\n",
       "       [ 0.67680544,  1.57189931],\n",
       "       [-0.36885733,  0.72886601],\n",
       "       [-1.02465495,  0.16190215],\n",
       "       [ 0.77455385,  0.15375803],\n",
       "       [ 1.4045116 , -0.00705701],\n",
       "       [-0.38147174, -0.23488747],\n",
       "       [ 0.72155224,  0.44721658],\n",
       "       [-0.51346686,  0.67869095],\n",
       "       [ 0.32118546,  0.28668667],\n",
       "       [ 0.1953628 ,  0.16085107],\n",
       "       [ 0.52824196,  0.98300993],\n",
       "       [-0.2216539 ,  0.25160139],\n",
       "       [ 0.22334676,  1.32217183],\n",
       "       [-0.10704572,  0.56178326],\n",
       "       [ 0.63651685,  0.75444825],\n",
       "       [-0.37227848,  0.99291317],\n",
       "       [ 0.20718083, -0.09767143],\n",
       "       [ 0.12733142, -0.3796549 ],\n",
       "       [ 0.71435231, -0.79994161],\n",
       "       [ 1.09487814, -0.36841845],\n",
       "       [-0.14814362,  0.96158657],\n",
       "       [ 1.586188  , -0.62984517],\n",
       "       [ 0.74444551,  0.57661371],\n",
       "       [ 2.18011028, -0.69977751],\n",
       "       [ 0.24575594,  0.8496383 ],\n",
       "       [-0.95003581,  0.90361699],\n",
       "       [-0.88230758,  0.07249044],\n",
       "       [ 2.02297079,  0.12325148],\n",
       "       [ 2.14577321,  0.46296362],\n",
       "       [ 0.35536468, -0.67847989],\n",
       "       [ 0.34665026,  1.11570676],\n",
       "       [ 1.7392373 ,  0.45900352],\n",
       "       [ 0.63856467, -0.44718443],\n",
       "       [ 1.2876687 , -0.4910366 ],\n",
       "       [-0.13772607,  1.2453262 ],\n",
       "       [-0.56175303,  1.05486051],\n",
       "       [ 1.29003748, -0.20691405],\n",
       "       [-0.87539365,  0.50543423],\n",
       "       [-0.92858249, -0.45631991],\n",
       "       [ 0.02493632,  0.10747958],\n",
       "       [ 0.1972559 , -0.06801668],\n",
       "       [ 0.73346056,  0.28161929],\n",
       "       [ 1.68294434, -0.2020423 ],\n",
       "       [ 0.50764124, -0.11731979],\n",
       "       [ 1.66760217, -0.42485665],\n",
       "       [-0.82172282,  0.63141066],\n",
       "       [ 0.30170903,  0.78603534],\n",
       "       [ 1.37671505, -0.80915107],\n",
       "       [ 1.17037551,  0.59840653],\n",
       "       [ 1.69945309,  0.58771967],\n",
       "       [ 0.21862323, -0.65252119],\n",
       "       [ 0.95291428, -0.41976564],\n",
       "       [-1.31850034,  0.42311235],\n",
       "       [-1.29681764,  0.18414709]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = datasets[0][0]\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe353d42198>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data1,data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (100,2) into shape (100)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-2f305cebe2ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/keras-env/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   2814\u001b[0m         \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2815\u001b[0m         plotnonfinite=plotnonfinite, **({\"data\": data} if data is not\n\u001b[0;32m-> 2816\u001b[0;31m         None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2817\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2818\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/keras-env/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1563\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1565\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/keras-env/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/keras-env/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4386\u001b[0m         \u001b[0;31m# np.ma.ravel yields an ndarray, not a masked array,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4387\u001b[0m         \u001b[0;31m# unless its argument is a masked array.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4388\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4389\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4390\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/keras-env/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, a, *args, **params)\u001b[0m\n\u001b[1;32m   6678\u001b[0m             \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6680\u001b[0;31m         \u001b[0mmarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6681\u001b[0m         \u001b[0mmethod_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6682\u001b[0m         \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/keras-env/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype)\u001b[0m\n\u001b[1;32m   7896\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7897\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7898\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmasked_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7899\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/keras-env/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, mask, dtype, copy, subok, ndmin, fill_value, keep_mask, hard_mask, shrink, order, **options)\u001b[0m\n\u001b[1;32m   2793\u001b[0m         \u001b[0;31m# Process data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2794\u001b[0m         _data = np.array(data, dtype=dtype, copy=copy,\n\u001b[0;32m-> 2795\u001b[0;31m                          order=order, subok=True, ndmin=ndmin)\n\u001b[0m\u001b[1;32m   2796\u001b[0m         \u001b[0m_baseclass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_baseclass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2797\u001b[0m         \u001b[0;31m# Check that we're not erasing the mask.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (100,2) into shape (100)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(datasets,datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
